TY - GEN
T1 - Prediction of rockburst based on an accident database
AU - Peixoto, Ana
AU - Sousa, Luis Ribeiro E.
AU - Sousa, Rita Leal E.
AU - Feng, Xia Ting
AU - Miranda, Tiago
AU - Martins, Francisco
PY - 2012
Y1 - 2012
N2 - Rockburst is characterized by a violent explosion of a certain block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoid and/or managed saving costs and possibly lives. In order to further understand the conditions that trigger rockburst, several cases of rockburst that occurred around the world were collected, stored in a database and analyzed. The analysis of the collected cases allowed one to build influence diagrams, listing the factors that interact and influence the occurrence of rockburst, as well as the relation between them. Data Mining (DM) techniques were also applied to the database cases in order to determine and conclude on relations between parameters that influence the occurrence of rockburst during underground construction.Arisk analysis methodologywas developed based on the use of Bayesian Networks (BN) and applied to the existing information of the database and some numerical applications were performed.
AB - Rockburst is characterized by a violent explosion of a certain block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoid and/or managed saving costs and possibly lives. In order to further understand the conditions that trigger rockburst, several cases of rockburst that occurred around the world were collected, stored in a database and analyzed. The analysis of the collected cases allowed one to build influence diagrams, listing the factors that interact and influence the occurrence of rockburst, as well as the relation between them. Data Mining (DM) techniques were also applied to the database cases in order to determine and conclude on relations between parameters that influence the occurrence of rockburst during underground construction.Arisk analysis methodologywas developed based on the use of Bayesian Networks (BN) and applied to the existing information of the database and some numerical applications were performed.
KW - Case studies
KW - Numerical modeling
KW - Risks and hazards
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UR - http://www.scopus.com/inward/citedby.url?scp=84856741232&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84856741232
SN - 9780415804448
T3 - Harmonising Rock Engineering and the Environment - Proceedings of the 12th ISRM International Congress on Rock Mechanics
SP - 1247
EP - 1252
BT - Harmonising Rock Engineering and the Environment - Proceedings of the 12th ISRM International Congress on Rock Mechanics
T2 - 12th International Congress on Rock Mechanics of the International Society for Rock Mechanics, ISRM 2011
Y2 - 18 October 2011 through 21 October 2011
ER -